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Background subtraction using competing models in the block-DCT domain

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2 Author(s)
Lamarre, M. ; Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada ; Clark, J.J.

Many image analysis applications rely on background subtraction as a pre-processing step. Hence it should be efficient and robust. We present a background subtraction algorithm that uses multiple competing hidden-Markov models (HMMs) over small neighbourhoods to maintain a locally valid background model in all situations. We use the DCT coefficients of JPEG encoded images directly to minimize computation and to use local information in a principled way. Region level processing is reduced to the minimum so that the extracted information that goes to higher level processing is unbiased.

Published in:

Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:1 )

Date of Conference:

2002